Overview

Dataset statistics

Number of variables15
Number of observations5546880
Missing cells4147574
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.8 MiB
Average record size in memory120.0 B

Variable types

Categorical2
Numeric13

Alerts

DATEUTC has a high cardinality: 52560 distinct valuesHigh cardinality
ID has a high cardinality: 108 distinct valuesHigh cardinality
LC_HUMIDITY is highly overall correlated with LC_RAD and 5 other fieldsHigh correlation
LC_DWPTEMP is highly overall correlated with LC_TEMP_QCL0 and 3 other fieldsHigh correlation
LC_RAD is highly overall correlated with LC_HUMIDITY and 1 other fieldsHigh correlation
LC_RAD60 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL0 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL1 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL2 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL3 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_HUMIDITY has 314899 (5.7%) missing valuesMissing
LC_DWPTEMP has 314899 (5.7%) missing valuesMissing
LC_n has 314899 (5.7%) missing valuesMissing
LC_RAD has 314899 (5.7%) missing valuesMissing
LC_RAININ has 314899 (5.7%) missing valuesMissing
LC_DAILYRAIN has 314899 (5.7%) missing valuesMissing
LC_WINDDIR has 314899 (5.7%) missing valuesMissing
LC_WINDSPEED has 314899 (5.7%) missing valuesMissing
LC_RAD60 has 277022 (5.0%) missing valuesMissing
LC_TEMP_QCL0 has 314899 (5.7%) missing valuesMissing
LC_TEMP_QCL1 has 345487 (6.2%) missing valuesMissing
LC_TEMP_QCL2 has 345487 (6.2%) missing valuesMissing
LC_TEMP_QCL3 has 345487 (6.2%) missing valuesMissing
LC_RAININ is highly skewed (γ1 = 46.73704986)Skewed
DATEUTC is uniformly distributedUniform
LC_RAD has 2610449 (47.1%) zerosZeros
LC_RAININ has 5068067 (91.4%) zerosZeros
LC_DAILYRAIN has 4333746 (78.1%) zerosZeros
LC_WINDDIR has 2022619 (36.5%) zerosZeros
LC_WINDSPEED has 2122964 (38.3%) zerosZeros
LC_RAD60 has 2542931 (45.8%) zerosZeros

Reproduction

Analysis started2023-04-24 15:04:28.978658
Analysis finished2023-04-24 15:07:04.641917
Duration2 minutes and 35.66 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DATEUTC
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct52560
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
2022-07-02 12:10:00
 
108
2022-09-01 09:00:00
 
108
2022-09-01 07:20:00
 
108
2022-09-01 07:30:00
 
108
2022-09-01 07:40:00
 
108
Other values (52555)
5546340 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters105390720
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01 00:10:00
2nd row2022-01-01 00:20:00
3rd row2022-01-01 00:30:00
4th row2022-01-01 00:40:00
5th row2022-01-01 00:50:00

Common Values

ValueCountFrequency (%)
2022-07-02 12:10:00 108
 
< 0.1%
2022-09-01 09:00:00 108
 
< 0.1%
2022-09-01 07:20:00 108
 
< 0.1%
2022-09-01 07:30:00 108
 
< 0.1%
2022-09-01 07:40:00 108
 
< 0.1%
2022-09-01 07:50:00 108
 
< 0.1%
2022-09-01 08:00:00 108
 
< 0.1%
2022-09-01 08:10:00 108
 
< 0.1%
2022-09-01 08:20:00 108
 
< 0.1%
2022-09-01 08:30:00 108
 
< 0.1%
Other values (52550) 5545800
> 99.9%

Length

2023-04-24T17:07:04.682018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:50:00 38520
 
0.3%
23:20:00 38520
 
0.3%
13:40:00 38520
 
0.3%
13:50:00 38520
 
0.3%
14:00:00 38520
 
0.3%
14:10:00 38520
 
0.3%
14:20:00 38520
 
0.3%
14:30:00 38520
 
0.3%
14:40:00 38520
 
0.3%
14:50:00 38520
 
0.3%
Other values (500) 10708560
96.5%

Most occurring characters

ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77656320
73.7%
Dash Punctuation 11093760
 
10.5%
Other Punctuation 11093760
 
10.5%
Space Separator 5546880
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32888304
42.4%
2 22415940
28.9%
1 8739802
 
11.3%
3 2876508
 
3.7%
5 2415744
 
3.1%
4 2400182
 
3.1%
7 1491264
 
1.9%
8 1491264
 
1.9%
6 1475712
 
1.9%
9 1461600
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 11093760
100.0%
Other Punctuation
ValueCountFrequency (%)
: 11093760
100.0%
Space Separator
ValueCountFrequency (%)
5546880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105390720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105390720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

ID
Categorical

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
LC-002
 
52560
LC-104
 
52560
LC-100
 
52560
LC-099
 
52560
LC-097
 
52560
Other values (103)
5284080 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters33281280
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLC-002
2nd rowLC-002
3rd rowLC-002
4th rowLC-002
5th rowLC-002

Common Values

ValueCountFrequency (%)
LC-002 52560
 
0.9%
LC-104 52560
 
0.9%
LC-100 52560
 
0.9%
LC-099 52560
 
0.9%
LC-097 52560
 
0.9%
LC-096 52560
 
0.9%
LC-095 52560
 
0.9%
LC-094 52560
 
0.9%
LC-092 52560
 
0.9%
LC-091 52560
 
0.9%
Other values (98) 5021280
90.5%

Length

2023-04-24T17:07:04.743822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lc-002 52560
 
0.9%
lc-031 52560
 
0.9%
lc-004 52560
 
0.9%
lc-005 52560
 
0.9%
lc-006 52560
 
0.9%
lc-008 52560
 
0.9%
lc-009 52560
 
0.9%
lc-010 52560
 
0.9%
lc-011 52560
 
0.9%
lc-012 52560
 
0.9%
Other values (98) 5021280
90.5%

Most occurring characters

ValueCountFrequency (%)
L 5546880
16.7%
C 5546880
16.7%
- 5546880
16.7%
0 5085360
15.3%
1 3116160
9.4%
2 1498320
 
4.5%
3 1184400
 
3.6%
4 1143360
 
3.4%
7 985680
 
3.0%
6 985680
 
3.0%
Other values (3) 2641680
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16640640
50.0%
Uppercase Letter 11093760
33.3%
Dash Punctuation 5546880
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5085360
30.6%
1 3116160
18.7%
2 1498320
 
9.0%
3 1184400
 
7.1%
4 1143360
 
6.9%
7 985680
 
5.9%
6 985680
 
5.9%
9 933120
 
5.6%
8 933120
 
5.6%
5 775440
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
L 5546880
50.0%
C 5546880
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 5546880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22187520
66.7%
Latin 11093760
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5546880
25.0%
0 5085360
22.9%
1 3116160
14.0%
2 1498320
 
6.8%
3 1184400
 
5.3%
4 1143360
 
5.2%
7 985680
 
4.4%
6 985680
 
4.4%
9 933120
 
4.2%
8 933120
 
4.2%
Latin
ValueCountFrequency (%)
L 5546880
50.0%
C 5546880
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33281280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 5546880
16.7%
C 5546880
16.7%
- 5546880
16.7%
0 5085360
15.3%
1 3116160
9.4%
2 1498320
 
4.5%
3 1184400
 
3.6%
4 1143360
 
3.4%
7 985680
 
3.0%
6 985680
 
3.0%
Other values (3) 2641680
7.9%

LC_HUMIDITY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean76.606512
Minimum12
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:04.808502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q164
median82
Q392
95-th percentile99
Maximum99
Range87
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.543738
Coefficient of variation (CV)0.24206478
Kurtosis-0.42356597
Mean76.606512
Median Absolute Deviation (MAD)12
Skewness-0.74770265
Sum4.0080381 × 108
Variance343.87024
MonotonicityNot monotonic
2023-04-24T17:07:04.861287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 332470
 
6.0%
96 160179
 
2.9%
95 158226
 
2.9%
94 156788
 
2.8%
97 153372
 
2.8%
93 150826
 
2.7%
92 147876
 
2.7%
91 141544
 
2.6%
90 141079
 
2.5%
98 137983
 
2.5%
Other values (78) 3551638
64.0%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 40
 
< 0.1%
14 223
 
< 0.1%
15 457
 
< 0.1%
16 649
< 0.1%
17 656
< 0.1%
18 683
< 0.1%
19 732
< 0.1%
20 1110
< 0.1%
21 1517
< 0.1%
ValueCountFrequency (%)
99 332470
6.0%
98 137983
2.5%
97 153372
2.8%
96 160179
2.9%
95 158226
2.9%
94 156788
2.8%
93 150826
2.7%
92 147876
2.7%
91 141544
2.6%
90 141079
2.5%

LC_DWPTEMP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3560
Distinct (%)0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean8.1499152
Minimum-12.6
Maximum59.78
Zeros4444
Zeros (%)0.1%
Negative433125
Negative (%)7.8%
Memory size42.3 MiB
2023-04-24T17:07:04.917744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-12.6
5-th percentile-1.44
Q14.26
median8.76
Q312.28
95-th percentile16.27
Maximum59.78
Range72.38
Interquartile range (IQR)8.02

Descriptive statistics

Standard deviation5.4557662
Coefficient of variation (CV)0.66942613
Kurtosis-0.37837472
Mean8.1499152
Median Absolute Deviation (MAD)3.96
Skewness-0.37391665
Sum42640201
Variance29.765385
MonotonicityNot monotonic
2023-04-24T17:07:04.972267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.39 9754
 
0.2%
8.89 9611
 
0.2%
9.28 9371
 
0.2%
9.22 9258
 
0.2%
9.11 9239
 
0.2%
9.5 9168
 
0.2%
9.61 9112
 
0.2%
6.61 8691
 
0.2%
9.78 8648
 
0.2%
9.72 8647
 
0.2%
Other values (3550) 5140482
92.7%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-12.6 3
< 0.1%
-12.5 2
< 0.1%
-12.4 1
 
< 0.1%
-12.39 1
 
< 0.1%
-12.34 1
 
< 0.1%
-12.33 2
< 0.1%
-12.3 2
< 0.1%
-12.29 1
 
< 0.1%
-12.28 2
< 0.1%
-12.25 2
< 0.1%
ValueCountFrequency (%)
59.78 10
< 0.1%
59.32 1
 
< 0.1%
59.05 1
 
< 0.1%
58.72 2
 
< 0.1%
41.61 1
 
< 0.1%
41.56 1
 
< 0.1%
34.39 1
 
< 0.1%
32.36 1
 
< 0.1%
30.23 1
 
< 0.1%
29.16 1
 
< 0.1%

LC_n
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean35.381164
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.033954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q137
median37
Q338
95-th percentile38
Maximum47
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.6904014
Coefficient of variation (CV)0.18909501
Kurtosis12.765319
Mean35.381164
Median Absolute Deviation (MAD)1
Skewness-3.6654634
Sum1.8511358 × 108
Variance44.761471
MonotonicityNot monotonic
2023-04-24T17:07:05.263876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
37 2148882
38.7%
38 2133692
38.5%
35 168968
 
3.0%
32 152968
 
2.8%
36 152725
 
2.8%
31 74293
 
1.3%
10 19945
 
0.4%
30 19039
 
0.3%
3 18727
 
0.3%
8 18494
 
0.3%
Other values (37) 324248
 
5.8%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
1 11336
0.2%
2 18208
0.3%
3 18727
0.3%
4 17387
0.3%
5 17305
0.3%
6 16371
0.3%
7 16408
0.3%
8 18494
0.3%
9 16379
0.3%
10 19945
0.4%
ValueCountFrequency (%)
47 4
 
< 0.1%
46 1
 
< 0.1%
45 7
 
< 0.1%
44 11
 
< 0.1%
43 15
 
< 0.1%
42 22
 
< 0.1%
41 28
 
< 0.1%
40 38
 
< 0.1%
39 3399
 
0.1%
38 2133692
38.5%

LC_RAD
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct926
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean79.722921
Minimum0
Maximum1017
Zeros2610449
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.318415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q385
95-th percentile447
Maximum1017
Range1017
Interquartile range (IQR)85

Descriptive statistics

Standard deviation144.51869
Coefficient of variation (CV)1.8127621
Kurtosis4.1749581
Mean79.722921
Median Absolute Deviation (MAD)1
Skewness2.198542
Sum4.1710881 × 108
Variance20885.651
MonotonicityNot monotonic
2023-04-24T17:07:05.373771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2610449
47.1%
1 44743
 
0.8%
4 37411
 
0.7%
3 33443
 
0.6%
2 31940
 
0.6%
5 27484
 
0.5%
6 22004
 
0.4%
7 20077
 
0.4%
23 19410
 
0.3%
22 19381
 
0.3%
Other values (916) 2365639
42.6%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 2610449
47.1%
1 44743
 
0.8%
2 31940
 
0.6%
3 33443
 
0.6%
4 37411
 
0.7%
5 27484
 
0.5%
6 22004
 
0.4%
7 20077
 
0.4%
8 18710
 
0.3%
9 17207
 
0.3%
ValueCountFrequency (%)
1017 1
< 0.1%
994 1
< 0.1%
975 1
< 0.1%
959 1
< 0.1%
956 1
< 0.1%
954 1
< 0.1%
950 2
< 0.1%
949 1
< 0.1%
948 2
< 0.1%
946 1
< 0.1%

LC_RAININ
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct110
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.00010391016
Minimum0
Maximum0.38
Zeros5068067
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.429394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.38
Range0.38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0010074327
Coefficient of variation (CV)9.6952279
Kurtosis8050.1865
Mean0.00010391016
Median Absolute Deviation (MAD)0
Skewness46.73705
Sum543.656
Variance1.0149207 × 10-6
MonotonicityNot monotonic
2023-04-24T17:07:05.483500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5068067
91.4%
0.002 52715
 
1.0%
0.001 45753
 
0.8%
0.003 26337
 
0.5%
0.004 11488
 
0.2%
0.005 6790
 
0.1%
0.006 5099
 
0.1%
0.007 3265
 
0.1%
0.008 2432
 
< 0.1%
0.009 1681
 
< 0.1%
Other values (100) 8354
 
0.2%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 5068067
91.4%
0.001 45753
 
0.8%
0.002 52715
 
1.0%
0.003 26337
 
0.5%
0.004 11488
 
0.2%
0.005 6790
 
0.1%
0.006 5099
 
0.1%
0.007 3265
 
0.1%
0.008 2432
 
< 0.1%
0.009 1681
 
< 0.1%
ValueCountFrequency (%)
0.38 1
< 0.1%
0.335 1
< 0.1%
0.167 1
< 0.1%
0.141 2
< 0.1%
0.129 1
< 0.1%
0.123 2
< 0.1%
0.114 1
< 0.1%
0.111 1
< 0.1%
0.11 1
< 0.1%
0.109 1
< 0.1%

LC_DAILYRAIN
Real number (ℝ)

MISSING  ZEROS 

Distinct144
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.0012207363
Minimum0
Maximum0.154
Zeros4333746
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.541404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.007
Maximum0.154
Range0.154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0051048583
Coefficient of variation (CV)4.1817864
Kurtosis337.27104
Mean0.0012207363
Median Absolute Deviation (MAD)0
Skewness14.203188
Sum6386.869
Variance2.6059579 × 10-5
MonotonicityNot monotonic
2023-04-24T17:07:05.596759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4333746
78.1%
0.002 181737
 
3.3%
0.001 118642
 
2.1%
0.003 81847
 
1.5%
0.004 80846
 
1.5%
0.007 72514
 
1.3%
0.005 60622
 
1.1%
0.006 54105
 
1.0%
0.008 36966
 
0.7%
0.009 28636
 
0.5%
Other values (134) 182320
 
3.3%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 4333746
78.1%
0.001 118642
 
2.1%
0.002 181737
 
3.3%
0.003 81847
 
1.5%
0.004 80846
 
1.5%
0.005 60622
 
1.1%
0.006 54105
 
1.0%
0.007 72514
 
1.3%
0.008 36966
 
0.7%
0.009 28636
 
0.5%
ValueCountFrequency (%)
0.154 7
 
< 0.1%
0.153 14
 
< 0.1%
0.152 97
 
< 0.1%
0.151 789
< 0.1%
0.15 116
 
< 0.1%
0.149 15
 
< 0.1%
0.148 10
 
< 0.1%
0.147 5
 
< 0.1%
0.146 13
 
< 0.1%
0.145 9
 
< 0.1%

LC_WINDDIR
Real number (ℝ)

MISSING  ZEROS 

Distinct360
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean-5.7257561
Minimum-179
Maximum180
Zeros2022619
Zeros (%)36.5%
Negative1714369
Negative (%)30.9%
Memory size42.3 MiB
2023-04-24T17:07:05.655034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile-158
Q1-56
median0
Q328
95-th percentile154
Maximum180
Range359
Interquartile range (IQR)84

Descriptive statistics

Standard deviation87.312361
Coefficient of variation (CV)-15.249054
Kurtosis-0.34460102
Mean-5.7257561
Median Absolute Deviation (MAD)41
Skewness0.026313425
Sum-29957047
Variance7623.4484
MonotonicityNot monotonic
2023-04-24T17:07:05.707353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2022619
36.5%
-146 13778
 
0.2%
-147 13770
 
0.2%
-144 13767
 
0.2%
-145 13690
 
0.2%
-143 13621
 
0.2%
-148 13606
 
0.2%
-149 13440
 
0.2%
-142 13402
 
0.2%
-140 13355
 
0.2%
Other values (350) 3086933
55.7%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-179 10578
0.2%
-178 10785
0.2%
-177 11081
0.2%
-176 10955
0.2%
-175 11332
0.2%
-174 11363
0.2%
-173 11644
0.2%
-172 11870
0.2%
-171 11991
0.2%
-170 12096
0.2%
ValueCountFrequency (%)
180 10609
0.2%
179 10390
0.2%
178 10234
0.2%
177 10109
0.2%
176 9867
0.2%
175 9803
0.2%
174 9685
0.2%
173 9745
0.2%
172 9771
0.2%
171 9676
0.2%

LC_WINDSPEED
Real number (ℝ)

MISSING  ZEROS 

Distinct892
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.28557705
Minimum0
Maximum13.7
Zeros2122964
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.764383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.04
Q30.31
95-th percentile1.4
Maximum13.7
Range13.7
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.56429441
Coefficient of variation (CV)1.9759795
Kurtosis20.145078
Mean0.28557705
Median Absolute Deviation (MAD)0.04
Skewness3.7023949
Sum1494133.7
Variance0.31842819
MonotonicityNot monotonic
2023-04-24T17:07:05.815909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2122964
38.3%
0.01 186275
 
3.4%
0.02 153706
 
2.8%
0.03 122616
 
2.2%
0.04 105635
 
1.9%
0.05 93342
 
1.7%
0.06 84198
 
1.5%
0.07 76651
 
1.4%
0.08 70054
 
1.3%
0.09 65109
 
1.2%
Other values (882) 2151431
38.8%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 2122964
38.3%
0.01 186275
 
3.4%
0.02 153706
 
2.8%
0.03 122616
 
2.2%
0.04 105635
 
1.9%
0.05 93342
 
1.7%
0.06 84198
 
1.5%
0.07 76651
 
1.4%
0.08 70054
 
1.3%
0.09 65109
 
1.2%
ValueCountFrequency (%)
13.7 1
< 0.1%
12.2 1
< 0.1%
12.15 1
< 0.1%
11.44 1
< 0.1%
11.21 1
< 0.1%
11.2 1
< 0.1%
10.71 1
< 0.1%
10.62 1
< 0.1%
10.45 1
< 0.1%
10.42 1
< 0.1%

LC_RAD60
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct868
Distinct (%)< 0.1%
Missing277022
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean79.6632
Minimum0
Maximum914
Zeros2542931
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-24T17:07:05.873255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q388
95-th percentile435
Maximum914
Range914
Interquartile range (IQR)88

Descriptive statistics

Standard deviation141.71334
Coefficient of variation (CV)1.7789059
Kurtosis3.8493839
Mean79.6632
Median Absolute Deviation (MAD)2
Skewness2.1298608
Sum4.1981375 × 108
Variance20082.67
MonotonicityNot monotonic
2023-04-24T17:07:05.925765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2542931
45.8%
1 82486
 
1.5%
2 49595
 
0.9%
3 41647
 
0.8%
4 39827
 
0.7%
5 26770
 
0.5%
6 22505
 
0.4%
7 20735
 
0.4%
8 18928
 
0.3%
27 18481
 
0.3%
Other values (858) 2405953
43.4%
(Missing) 277022
 
5.0%
ValueCountFrequency (%)
0 2542931
45.8%
1 82486
 
1.5%
2 49595
 
0.9%
3 41647
 
0.8%
4 39827
 
0.7%
5 26770
 
0.5%
6 22505
 
0.4%
7 20735
 
0.4%
8 18928
 
0.3%
9 18414
 
0.3%
ValueCountFrequency (%)
914 1
< 0.1%
900 1
< 0.1%
896 1
< 0.1%
894 1
< 0.1%
884 1
< 0.1%
882 2
< 0.1%
878 1
< 0.1%
876 1
< 0.1%
873 1
< 0.1%
870 1
< 0.1%

LC_TEMP_QCL0
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5349
Distinct (%)0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean12.777789
Minimum-11.7
Maximum60
Zeros7227
Zeros (%)0.1%
Negative175303
Negative (%)3.2%
Memory size42.3 MiB
2023-04-24T17:07:05.980838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile1
Q17.38
median12.39
Q317.89
95-th percentile25.77
Maximum60
Range71.7
Interquartile range (IQR)10.51

Descriptive statistics

Standard deviation7.6258419
Coefficient of variation (CV)0.59680448
Kurtosis-0.15275933
Mean12.777789
Median Absolute Deviation (MAD)5.28
Skewness0.20711242
Sum66853152
Variance58.153465
MonotonicityNot monotonic
2023-04-24T17:07:06.034272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89 11822
 
0.2%
9.78 11787
 
0.2%
10 11707
 
0.2%
10.5 11097
 
0.2%
9.72 11073
 
0.2%
10.11 10755
 
0.2%
10.28 10739
 
0.2%
10.22 10696
 
0.2%
8.28 10695
 
0.2%
9.61 10655
 
0.2%
Other values (5339) 5120955
92.3%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-11.7 3
< 0.1%
-11.66 2
< 0.1%
-11.61 3
< 0.1%
-11.6 2
< 0.1%
-11.56 1
 
< 0.1%
-11.55 1
 
< 0.1%
-11.54 1
 
< 0.1%
-11.5 4
< 0.1%
-11.47 1
 
< 0.1%
-11.45 2
< 0.1%
ValueCountFrequency (%)
60 10
< 0.1%
59.5 1
 
< 0.1%
59.28 1
 
< 0.1%
58.89 2
 
< 0.1%
43.76 1
 
< 0.1%
43.71 1
 
< 0.1%
43.53 1
 
< 0.1%
43.5 2
 
< 0.1%
43.49 1
 
< 0.1%
43.44 1
 
< 0.1%

LC_TEMP_QCL1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5326
Distinct (%)0.1%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.76951
Minimum-11.7
Maximum43.11
Zeros7132
Zeros (%)0.1%
Negative174851
Negative (%)3.2%
Memory size42.3 MiB
2023-04-24T17:07:06.092051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile1
Q17.37
median12.39
Q317.89
95-th percentile25.75
Maximum43.11
Range54.81
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation7.6205306
Coefficient of variation (CV)0.59677551
Kurtosis-0.15715431
Mean12.76951
Median Absolute Deviation (MAD)5.28
Skewness0.20437188
Sum66419238
Variance58.072487
MonotonicityNot monotonic
2023-04-24T17:07:06.143290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89 11756
 
0.2%
9.78 11678
 
0.2%
10 11612
 
0.2%
10.5 11029
 
0.2%
9.72 11006
 
0.2%
10.28 10660
 
0.2%
10.11 10659
 
0.2%
8.28 10633
 
0.2%
10.22 10626
 
0.2%
9.61 10570
 
0.2%
Other values (5316) 5091164
91.8%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.7 3
< 0.1%
-11.66 2
< 0.1%
-11.61 3
< 0.1%
-11.6 2
< 0.1%
-11.56 1
 
< 0.1%
-11.55 1
 
< 0.1%
-11.54 1
 
< 0.1%
-11.5 4
< 0.1%
-11.47 1
 
< 0.1%
-11.45 2
< 0.1%
ValueCountFrequency (%)
43.11 1
< 0.1%
42.94 1
< 0.1%
42.87 1
< 0.1%
42.83 1
< 0.1%
42.69 1
< 0.1%
42.62 1
< 0.1%
42.51 1
< 0.1%
42.48 1
< 0.1%
42.44 1
< 0.1%
42.43 1
< 0.1%

LC_TEMP_QCL2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92170
Distinct (%)1.8%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.771063
Minimum-11.7
Maximum43.197
Zeros67
Zeros (%)< 0.1%
Negative177664
Negative (%)3.2%
Memory size42.3 MiB
2023-04-24T17:07:06.199802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile0.9925
Q17.37
median12.4
Q317.89
95-th percentile25.761
Maximum43.197
Range54.897
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation7.6199829
Coefficient of variation (CV)0.59666001
Kurtosis-0.15828887
Mean12.771063
Median Absolute Deviation (MAD)5.276
Skewness0.20587398
Sum66427320
Variance58.064139
MonotonicityNot monotonic
2023-04-24T17:07:06.250508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 1748
 
< 0.1%
10.3 1719
 
< 0.1%
10.1 1719
 
< 0.1%
11.8 1648
 
< 0.1%
13.3 1627
 
< 0.1%
10.2 1608
 
< 0.1%
10.7 1544
 
< 0.1%
13.2 1499
 
< 0.1%
11.1 1467
 
< 0.1%
11.3 1464
 
< 0.1%
Other values (92160) 5185350
93.5%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.7 2
< 0.1%
-11.6 1
 
< 0.1%
-11.5 2
< 0.1%
-11.4655 1
 
< 0.1%
-11.4345 2
< 0.1%
-11.4 2
< 0.1%
-11.3845 3
< 0.1%
-11.3555 1
 
< 0.1%
-11.3245 1
 
< 0.1%
-11.3145 1
 
< 0.1%
ValueCountFrequency (%)
43.197 1
< 0.1%
43.027 1
< 0.1%
42.929 1
< 0.1%
42.917 1
< 0.1%
42.749 1
< 0.1%
42.707 1
< 0.1%
42.542 1
< 0.1%
42.539 2
< 0.1%
42.522 1
< 0.1%
42.519 1
< 0.1%

LC_TEMP_QCL3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4567782
Distinct (%)87.8%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.606175
Minimum-11.4
Maximum42.161164
Zeros0
Zeros (%)0.0%
Negative177244
Negative (%)3.2%
Memory size42.3 MiB
2023-04-24T17:07:06.333407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile1.0325705
Q17.387517
median12.349102
Q317.66019
95-th percentile25.004656
Maximum42.161164
Range53.561164
Interquartile range (IQR)10.272673

Descriptive statistics

Standard deviation7.3911548
Coefficient of variation (CV)0.58631224
Kurtosis-0.18548579
Mean12.606175
Median Absolute Deviation (MAD)5.1488284
Skewness0.1501703
Sum65569671
Variance54.62917
MonotonicityNot monotonic
2023-04-24T17:07:06.389801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 1400
 
< 0.1%
10.2 1360
 
< 0.1%
11.9 1335
 
< 0.1%
10.3 1334
 
< 0.1%
10.9 1333
 
< 0.1%
13.3 1333
 
< 0.1%
13.4 1310
 
< 0.1%
11.3 1288
 
< 0.1%
13.6 1282
 
< 0.1%
10.1 1281
 
< 0.1%
Other values (4567772) 5188137
93.5%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.4 3
< 0.1%
-11.3 2
< 0.1%
-11.2 1
 
< 0.1%
-11.18447 1
 
< 0.1%
-11.17442 2
< 0.1%
-11.12442 1
 
< 0.1%
-11.1 1
 
< 0.1%
-11.09022 2
< 0.1%
-11.07447 1
 
< 0.1%
-11.05952 1
 
< 0.1%
ValueCountFrequency (%)
42.16116354 1
< 0.1%
42.12416033 1
< 0.1%
42.02266235 1
< 0.1%
41.95165925 1
< 0.1%
41.93676939 1
< 0.1%
41.85961032 1
< 0.1%
41.83010052 1
< 0.1%
41.70123848 1
< 0.1%
41.36824861 1
< 0.1%
41.23725765 1
< 0.1%

Interactions

2023-04-24T17:06:39.850486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:40.864346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:46.390106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:51.399561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:56.358516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:01.199125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:06.140510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:10.897890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:15.856586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:20.638658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:25.516822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:30.399595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:35.064710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:40.306022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:41.419137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:46.731114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:51.759132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:56.721486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:01.558860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:06.518928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:11.273341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:16.213831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:21.002602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:25.868792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:30.759768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:35.427249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:40.715358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:41.942645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:47.107440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:52.089417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:57.084939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:01.926908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:06.892131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:11.648672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:16.576162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:21.372100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:26.231117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:31.136324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:35.796749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:41.103953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:42.395376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:47.477693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:52.458039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:57.423108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:02.278221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:07.241269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:12.015467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:16.932065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:21.753925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:26.588261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:31.494843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:36.143471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:41.487865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:42.812966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:47.856338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:52.849413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:57.814230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:02.612133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:07.594499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:12.401619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:17.312871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:22.122117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:26.941984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:31.848720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:36.501590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:41.895779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:43.217048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:48.287917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:53.251110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:58.199034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:02.984656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:07.930892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:12.767951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:17.705028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:22.510019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:27.311981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:32.214426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:36.884992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:42.292825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:43.624949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:48.689414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:53.641470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:58.574930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:03.351482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:08.288634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:13.123190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:18.082749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:22.886565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:27.694235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:32.588627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:37.259554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:42.696234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:43.998537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:49.097381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:54.039360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:58.944113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:03.947156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:08.657092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:13.521268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:18.426152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:23.260693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:28.062742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:32.942999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:37.630717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:43.093963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:44.392513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:49.494986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:54.456468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:59.333128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:04.314577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:09.030041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:13.916303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:18.786459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:23.677086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:28.589102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:33.315856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:38.001320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:43.467698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:44.798826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:49.859660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:54.833625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:59.712406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:04.669516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:09.392760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:14.288210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:19.149179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:24.041250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:28.936806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:33.669300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:38.354411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:43.851406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:45.213518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:50.231683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:55.199934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:00.092074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:05.023106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:09.776220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:14.702469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:19.518837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:24.397217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:29.315901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:34.001550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:38.739396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:44.226084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:45.589005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:50.601038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:55.567453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:00.452645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:05.372369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:10.143427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:15.077756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:19.881432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:24.757970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:29.677476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:34.350652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:39.071634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:44.557376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:45.991704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:50.962919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:05:55.942973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:00.812584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:05.741272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:10.490915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:15.452498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:20.236523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:25.112189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:30.031293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:34.712908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:06:39.440640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-24T17:07:06.452434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
LC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3
LC_HUMIDITY1.000-0.1690.015-0.5530.1780.262-0.048-0.292-0.608-0.641-0.641-0.638-0.625
LC_DWPTEMP-0.1691.000-0.0050.2250.0430.079-0.027-0.0080.2410.8300.8300.8310.840
LC_n0.015-0.0051.000-0.0100.001-0.006-0.004-0.017-0.010-0.009-0.009-0.008-0.008
LC_RAD-0.5530.225-0.0101.000-0.053-0.052-0.0010.3480.9540.4950.4950.4940.482
LC_RAININ0.1780.0430.001-0.0531.0000.360-0.0200.061-0.048-0.048-0.048-0.048-0.046
LC_DAILYRAIN0.2620.079-0.006-0.0520.3601.000-0.0660.135-0.035-0.072-0.072-0.071-0.068
LC_WINDDIR-0.048-0.027-0.004-0.001-0.020-0.0661.000-0.092-0.0010.0140.0140.0140.012
LC_WINDSPEED-0.292-0.008-0.0170.3480.0610.135-0.0921.0000.3540.1410.1420.1400.136
LC_RAD60-0.6080.241-0.0100.954-0.048-0.035-0.0010.3541.0000.5370.5370.5370.522
LC_TEMP_QCL0-0.6410.830-0.0090.495-0.048-0.0720.0140.1410.5371.0001.0001.0000.999
LC_TEMP_QCL1-0.6410.830-0.0090.495-0.048-0.0720.0140.1420.5371.0001.0001.0000.999
LC_TEMP_QCL2-0.6380.831-0.0080.494-0.048-0.0710.0140.1400.5371.0001.0001.0000.999
LC_TEMP_QCL3-0.6250.840-0.0080.482-0.046-0.0680.0120.1360.5220.9990.9990.9991.000

Missing values

2023-04-24T17:06:45.525855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-24T17:06:50.285911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-24T17:07:02.178311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3
02022-01-01 00:10:00LC-00292.011.7838.00.00.00.0-169.00.430.013.1113.1113.051513.048027
12022-01-01 00:20:00LC-00292.011.7337.00.00.00.0-170.00.330.013.0113.0112.951512.985849
22022-01-01 00:30:00LC-00292.011.7338.00.00.00.0-167.00.460.013.0013.0012.941512.950322
32022-01-01 00:40:00LC-00292.011.7237.00.00.00.0-160.00.520.013.0013.0012.941512.949550
42022-01-01 00:50:00LC-00292.011.7238.00.00.00.0-166.00.510.013.0013.0012.941512.952268
52022-01-01 01:00:00LC-00292.011.7237.00.00.00.0-158.00.930.013.0013.0012.941512.938731
62022-01-01 01:10:00LC-00292.011.7138.00.00.00.0-161.00.540.013.0013.0012.941512.949960
72022-01-01 01:20:00LC-00291.011.6237.00.00.00.0-163.00.710.013.0013.0012.941512.960576
82022-01-01 01:30:00LC-00291.011.6138.00.00.00.0-160.00.540.013.0013.0012.941512.980432
92022-01-01 01:40:00LC-00291.011.6137.00.00.00.0-163.00.850.013.0013.0012.941512.963181
DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3
55468702022-12-31 22:30:00LC-13853.06.4932.00.00.00.002-70.01.450.016.2016.2016.4516.39359
55468712022-12-31 22:40:00LC-13852.06.3231.00.00.00.002-52.01.230.016.2916.2916.5416.48690
55468722022-12-31 22:50:00LC-13851.06.3032.00.00.00.002-64.01.040.016.4316.4316.6816.63222
55468732022-12-31 23:00:00LC-13850.06.2131.00.00.00.002-61.01.430.016.5016.5016.7516.71759
55468742022-12-31 23:10:00LC-13850.06.1632.00.00.00.002-62.01.370.016.5616.5616.8116.77703
55468752022-12-31 23:20:00LC-13850.06.0532.00.00.00.000-49.00.670.016.5116.5116.7616.76285
55468762022-12-31 23:30:00LC-13850.06.0032.00.00.00.000-65.01.390.016.4816.4816.7316.70722
55468772022-12-31 23:40:00LC-13850.05.9031.00.00.00.000-41.01.250.016.3716.3716.6216.60001
55468782022-12-31 23:50:00LC-13850.05.8932.00.00.00.000-51.01.220.016.2716.2716.5216.50053
55468792023-01-01 00:00:00LC-13850.05.7831.00.00.00.000-53.01.080.016.1816.1816.4316.37461